“The observer is a participant, as the great revolution in quantum physics has taught us.”
― Will Eaves, Murmur.

Will Eaves short story ‘Murmur’ is a fictive account of the life of the British scientist Alan Turing. Personifying the individual struggles of Turing against the bureaucratic violence of his time, the story also does a remarkable job of intertwining the scientist’s research with his intuition about the future. At one point, Turing wonders at the possibility of one of his inventions being placed at a carnival in the far future. This invention would be extraordinary, for encompassing the ambiguity of a future in which it is difficult to delineate between machines and humans.

 ‘Are you a man or a machine? And the answer will be both.’ Eaves (2018).

As these lines suggest, the scientist conceived by Will Eaves does not see the Turing Test (commonly described as the question of a machine’s ability to exhibit intelligent thought, indistinguishable from a human Turing 1950) as a dilemma but as a matter-of-fact irreconcilability of the future. The ends of human thought and the beginnings of decision-making by machines would be indivisible.

Today, this future run by ‘thinking machines’ is still in the making. However, the problem faced by modern society is not yet that of machines surpassing human intelligence, but more archaically grounded in how to dissuade humans from acquiring too much. Regulation of the old-age issue of monopolies still precedes and now intertwines with the issue of AI and decision-making algorithms.

The monopolistic rise of platform companies highlights that governance of data is intrinsically linked to how the operation of such platforms can touch upon every aspect of everyday life. The term ‘platform’ highlights the intermediary role of these companies in connecting different users, such as consumers and sellers, advertisers and software producers, as well as equipping these users with tools to build their own marketplaces (Evans and Schmalensee 2008). Platformisation can be understood as the ability of these companies to provide a host of interconnected services, which enable users to be ‘locked in’ or depend on these companies for the majority of their consumption. In comparison to traditional firms, platform companies exhibit high monopolistic tendencies owing to ‘network effects’, which in turn capacitates them to construct an ecosystem of ancillary services surrounding their initial business model (Srnicek 2017; Gawer 2014).

A key issue with data governance, which is especially visible in relation to the rise of platform companies is that whilst all kinds of data can be commodified, the varying methodologies of commodification have not yet been accompanied with complementary regulation. The lack of data regulation is enabling multiplicities of value extraction, which is exercised in different ways and ultimately benefits only a few platform companies globally. To better understand the lag between governance and monopolisation of data by companies, the following discussion introduces the term ‘intellectual monopoly’ as used by emerging literature and focuses on the limitations of current data privacy regulation in controlling intellectual monopoly extraction.

The intellectual monopoly defined

The term ‘intellectual monopoly’ summarises the inordinate ability of platform companies to ride and dominate the current data revolution. Intellectual monopolies have become the “dominant form of organisation of big business” (Pagano, 2014), with the largest firms in the world by market capitalisation of the likes of Alibaba, Alphabet, Amazon, Apple, Facebook etc. all dependent on IPR-related rents as amongst the primary sources of their operating revenues.

An intellectual monopoly emerges from where products derived from intangibles are controlled by the custodians of those intangibles, to result in an ongoing ability of the custodians to continue to benefit from such control and ownership, over the lifecycles of those products (Schwartz, 2016; Birch 2019; Durand and Milberg 2019; Rikap 2020), Such monopolies are contingent on the construction of knowledge as intellectual property by the assertion of private ownership rights and particularly intellectual property rights (IPRs) over such knowledge and the inspection by various means of excludability, into the ordinarily non-rival nature of such knowledge as a commodity. Excludability, in this context, extends to both consumers and competitors, with a view to the creation of real market power and the ability of the intellectual monopoly to preclude and stifle subsequent innovation by others derivative of the monopolised knowledge. Simultaneously, excludability allows the intellectual monopoly to entrench its dominant position in the markets for speculative products, resultant of the monopolised knowledge, and for the monopolised knowledge itself.

For example, in Case T-201/104, Microsoft Corp. v. Commission, [2007] 5 C.M.L.R. 846, Microsoft sought to avoid making its technology available to competitors by claiming that doing so “would … eliminate future incentives to invest in the creation of more intellectual property” or in other words reduce Microsoft’s incentive to develop that technology. This argument was rejected for being “vague, general, and purely theoretical” as the prospective technologies or products were not identified or specified by Microsoft.

Intellectual monopoly and privacy

Existent data privacy protections highlight yet another facet of the regulatory gap in addressing intellectual monopolies. This is particular, as data of any kind generally cannot itself be legally owned as intellectual property; and it cannot be subject to copyright, trademark or patent laws as discussed in Oxford v Moss (1979) and others. Intellectual monopolies rely on the availability of “para-copyright” or the means to make data excludable to others, as their sole basis to monopolise such data. This becomes inequitable from the perspective of the actual authors of such data, as their data can and does result in the generation of value and in and of itself also therefore has value. Data can thus be argued to also have “property-like” characteristics, even if it cannot legally be considered to be property in its own right.

The most prominent examples of regulatory regimes addressing data privacy are the General Data Protection Regulation (Regulation (EC) 2016/679) (‘GDPR’) and the California Consumer Privacy Act of 2018 (‘CCPA’). These regulations generally offer protections for the personal data of natural persons, defined as “data-subjects” and “consumers”.

Such personal data in the case of the GDPR consists of “personally identifiable information” including personal names, addresses and location data, online identifiers such as IP addresses or cookie identification as well as medical, genetic and biometric data, sexual or lifestyle/orientation, health data, and criminal records etc. The rights afforded to data-subjects, vis-à-vis their personal data relate to the use of such being aligned with the expectations of the data-subject/consumer, for them to be informed of such use, to be able to access, erase or rectify their data and so on. These rights do not include any kind of general restrictions on the sale of personal data under the GDPR but are only limited to the “data controller” having a lawful basis for the processing. The CCPA goes further giving “consumers” the right to opt out the sale of their personal data.

For the purposes of intellectual monopoly, regulations of the likes of GDPR or the CCPA do not extend to any other kind of data directly or indirectly generated by data-subjects, beyond their personal data. Ownership of even personal data by the data-subject is not acknowledged. Secure control and technical means (para-copyright), presumably in conformity with Directive 96/9/EC (database directive) is assumed, particularly under the GDPR.

The “property-like” characteristics of data, by way the ability of the data-custodian to sell collected data and presumably employ such data to innovate and accrue rents (subject to relevant compliance with the regulatory framework) are fully recognised. However, rent and value capture by intellectual monopolies, by employing consumer and data-subject generated non-personal data, is not addressed in either the GDPR or the CCPA. In this regard, regulatory privacy protections do not impede or indeed even seek to regulate the behaviours of intellectual monopolies in relation to different kinds of rents and add very little to the body of competition law (See Khan 2016; Coyle 2020).

Given that intellectual monopolies do not promulgate wealth and resource distribution even within the framework of the conventional capitalist model, and incline towards dissonance from productivity-enhancing investment in different ways (See Azmeh and Foster 2016; Sial 2020), the regulation of such monopolies to accrue ever increasing inordinate rents requires a robust and holistic set of regulations. The quadrupling of wealth by these companies under the current conditions of a global pandemic substantiates these concerns and should be seen as a primer for future expansion, unless controlled through corresponding regulations.

Such regulation should move away data monopolisation and even ideas of corporate “data-stewardship” towards strategies that empower the public domain (See Shah 2018; Savona 2020).

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Notes:

  • This blog post expresses the views of its author(s), not the position of LSE Business Review or the London School of Economics.
  • Featured image by geralt, under a Pixabay licence
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Farwa Sial is a post-doc research associate in the Global Development Institute, University of Manchester.